Can AI Chatbots Help Product Managers Do More With Less?
AI Chatbots are being talked about everywhere. Product managers rely heavily on collaborations with stakeholders to achieve success with their strategic objectives. These stakeholders include developers, sales teams, marketing teams, executives, and other key players. Yet these stakeholders alone do not guarantee success for your customers and clients. As a product manager, you need to use all the tools at your disposal to add value to their strategies and goals.
Recent headlines tout the use of artificial intelligence (AI) and AI chatbots as key tools for companies to meet goals and ensure their products succeed in marketplaces. But are the risks worth the potential payoff?
Challenges Facing Today’s Product Managers
Generating content for products during their development cycles is challenging. Teams often face intense pressure to produce accurate, compelling content during a product development cycle. Some of the reports a product manager is expected to produce—and deliver with short turnaround times—are accurate sales forecasts and predictive analytics models outlining customer needs.
Add to all this the fact that product managers don’t want to saddle their development teams with cumbersome or time-consuming content requests.
So, how do they produce all these forecasts quickly, with accuracy?
The Rise of AI Chatbots
By now everyone has heard that AI and chatbots can be cutting-edge tools that can deliver surprising results. Now embedded analytics users are starting to wonder if they can deploy AI to bring value to their customers.
Embedded analytics users need real-time insights and cutting-edge processes to deploy their tools’ full capabilities. To stay on the cutting edge, product managers are investigating if AI technology can integrate with their embedded analytics solutions, especially if they already have an existing relationship with AI.
There is much to be learned about the analytics potential of tools such as ChatGPT. Follow along below as we dig into the good and bad of AI technology for your platform’s analytics.
The Pros and Cons of AI Chatbots
The Cons
Both enterprise and individual users can deploy AI chatbots like ChatGPT to enhance content, manipulate text, and summarize information with minimal investment. However, there are risks associated with using ChatGPT, especially for inexperienced users who may overlook limitations related to data, security, and analytics.
May Lack Substantial Value or Contain False Statements
One primary risk when creating new content using ChatGPT is that it can produce eloquent prose but may lack substantial value or contain false statements. Users must thoroughly assess the AI-generated text for accuracy, appropriateness, and usefulness before publishing it.
Moreover, AI chatbots, including ChatGPT, have a significant limitation in that they do not incorporate real-time data or recent historical events. The current version of ChatGPT, trained in December 2021, does not include the most up-to-date information available.
Three Scenarios for Regulatory, Data, and Privacy Concerns
- Regulatory: Your customers may have to follow regulatory guidelines that may restrict or regulate how they navigate AI chatbot technology. What are the challenges of juggling global regulations that may differ by market rather than by product?
- Data: In this scenario, a company’s data cannot risk being compromised. Are they open to putting their data into a cloud environment where it may not be as sure as they want it to be?
- Privacy: Perhaps they have privacy issues that have kept them on premises. How would they feel about integrating AI chatbot tools? Is ironclad privacy and security too much to ask of a relatively new technology such as ChatGPT?
The Pros
Ability to Generate Queries Quickly
The upsides of AI include ChatGPT’s ability to generate queries quickly. During a recent Logi Symphony innovation session, a ChatGPT integration was performed that combined the powers of the data cubes that Python transforms to make live day queries. The results came in very fast. It was not a matter of weeks for the queries to be finalized. Rather, it was only a single day that automated insights from the managed charts and dashboards were returned.
If these results are typical of AI’s integration with embedded analytics customers’ capabilities, a product manager may indeed seek more and more AI solutions that provide insights or explains how the decisions or predictions are made during a product development cycle.
What’s Next?
For embedded analytics users, there is a lot to learn about how AI can elevate your platform’s analytics capabilities. This is why insightsoftware has a video on the topic. Delivered by our Logi Symphony team, the video tackles all these questions and more.
You can access the video here.